Determinação de constituintes químicos em madeira de eucalipto por PI-CG/EM e calibração multivariada: comparação entre redes neurais artificiais e máquinas de vetor suporte

• Autor(a) principal: Nunes, Cleiton Antônio
• Data de Publicação: 2010
• Outros Autores: Lima, Claudio Ferreira, Barbosa, Luiz Cláudio de Almeida, Colodette, Jorge Luiz, Fidêncio, Paulo Henrique
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/4833
• topic: Analytical pyrolysis; Artificial neural network; Least square-support vector machine

Multivariate models were developed using Artificial Neural Network (ANN) and Least Square - Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by pyrolysis associated to gaseous chromatography and mass spectrometry (Py-GC/MS). The results obtained by two calibration methods were in agreement with those of reference methods. However a comparison indicated that the LS-SVM model presented better predictive capacity for the cellulose and lignin contents, while the ANN model presented was more adequate for estimating the hemicelluloses content and lignin siringyl/guaiacyl ratio.